Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory222.6 KiB
Average record size in memory104.1 B

Variable types

Numeric12
Categorical1

Alerts

cloud is highly overall correlated with humidity and 2 other fieldsHigh correlation
dewpoint is highly overall correlated with maxtemp and 4 other fieldsHigh correlation
humidity is highly overall correlated with cloud and 1 other fieldsHigh correlation
maxtemp is highly overall correlated with dewpoint and 5 other fieldsHigh correlation
mintemp is highly overall correlated with dewpoint and 4 other fieldsHigh correlation
pressure is highly overall correlated with dewpoint and 4 other fieldsHigh correlation
rainfall is highly overall correlated with cloud and 1 other fieldsHigh correlation
sunshine is highly overall correlated with cloud and 4 other fieldsHigh correlation
temparature is highly overall correlated with dewpoint and 5 other fieldsHigh correlation
winddirection is highly overall correlated with dewpoint and 4 other fieldsHigh correlation
id is uniformly distributed Uniform
day is uniformly distributed Uniform
id has unique values Unique
sunshine has 337 (15.4%) zeros Zeros

Reproduction

Analysis started2025-03-17 00:58:33.979485
Analysis finished2025-03-17 00:59:06.283344
Duration32.3 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct2190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094.5
Minimum0
Maximum2189
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:06.432695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile109.45
Q1547.25
median1094.5
Q31641.75
95-th percentile2079.55
Maximum2189
Range2189
Interquartile range (IQR)1094.5

Descriptive statistics

Standard deviation632.34287
Coefficient of variation (CV)0.57774588
Kurtosis-1.2
Mean1094.5
Median Absolute Deviation (MAD)547.5
Skewness0
Sum2396955
Variance399857.5
MonotonicityStrictly increasing
2025-03-17T00:59:06.705551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1462 1
 
< 0.1%
1456 1
 
< 0.1%
1457 1
 
< 0.1%
1458 1
 
< 0.1%
1459 1
 
< 0.1%
1460 1
 
< 0.1%
1461 1
 
< 0.1%
1463 1
 
< 0.1%
1505 1
 
< 0.1%
Other values (2180) 2180
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2189 1
< 0.1%
2188 1
< 0.1%
2187 1
< 0.1%
2186 1
< 0.1%
2185 1
< 0.1%
2184 1
< 0.1%
2183 1
< 0.1%
2182 1
< 0.1%
2181 1
< 0.1%
2180 1
< 0.1%

day
Real number (ℝ)

Uniform 

Distinct365
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.9484
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:06.967474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q189
median178.5
Q3270
95-th percentile346
Maximum365
Range364
Interquartile range (IQR)181

Descriptive statistics

Standard deviation105.20359
Coefficient of variation (CV)0.58463199
Kurtosis-1.1930053
Mean179.9484
Median Absolute Deviation (MAD)90.5
Skewness0.030615044
Sum394087
Variance11067.796
MonotonicityNot monotonic
2025-03-17T00:59:07.175143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 7
 
0.3%
144 7
 
0.3%
267 7
 
0.3%
140 7
 
0.3%
111 7
 
0.3%
112 7
 
0.3%
236 7
 
0.3%
135 7
 
0.3%
82 7
 
0.3%
265 7
 
0.3%
Other values (355) 2120
96.8%
ValueCountFrequency (%)
1 6
0.3%
2 6
0.3%
3 7
0.3%
4 7
0.3%
5 7
0.3%
6 7
0.3%
7 7
0.3%
8 7
0.3%
9 7
0.3%
10 6
0.3%
ValueCountFrequency (%)
365 5
0.2%
364 5
0.2%
363 5
0.2%
362 6
0.3%
361 6
0.3%
360 6
0.3%
359 5
0.2%
358 5
0.2%
357 6
0.3%
356 6
0.3%

pressure
Real number (ℝ)

High correlation 

Distinct236
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.6021
Minimum999
Maximum1034.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:07.381261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum999
5-th percentile1005.9
Q11008.6
median1013
Q31017.775
95-th percentile1023.3
Maximum1034.6
Range35.6
Interquartile range (IQR)9.175

Descriptive statistics

Standard deviation5.6553657
Coefficient of variation (CV)0.0055794729
Kurtosis-0.50299885
Mean1013.6021
Median Absolute Deviation (MAD)4.5
Skewness0.2840623
Sum2219788.7
Variance31.983161
MonotonicityNot monotonic
2025-03-17T00:59:07.581734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008.1 64
 
2.9%
1008.5 48
 
2.2%
1008.4 48
 
2.2%
1016.8 46
 
2.1%
1008.9 42
 
1.9%
1013 41
 
1.9%
1011.4 38
 
1.7%
1012.5 36
 
1.6%
1017.1 35
 
1.6%
1007.9 33
 
1.5%
Other values (226) 1759
80.3%
ValueCountFrequency (%)
999 5
0.2%
999.6 1
 
< 0.1%
1000 3
0.1%
1000.1 2
 
0.1%
1000.2 3
0.1%
1000.3 2
 
0.1%
1000.7 1
 
< 0.1%
1001 2
 
0.1%
1001.4 1
 
< 0.1%
1001.5 1
 
< 0.1%
ValueCountFrequency (%)
1034.6 3
0.1%
1032.3 1
 
< 0.1%
1027.9 2
0.1%
1027.8 1
 
< 0.1%
1027.6 1
 
< 0.1%
1027.5 1
 
< 0.1%
1027.4 2
0.1%
1027.2 1
 
< 0.1%
1027.1 2
0.1%
1026.9 2
0.1%

maxtemp
Real number (ℝ)

High correlation 

Distinct219
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.365799
Minimum10.4
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:08.223963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.4
5-th percentile16.5
Q121.3
median27.8
Q331.2
95-th percentile33.3
Maximum36
Range25.6
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation5.6543304
Coefficient of variation (CV)0.21445701
Kurtosis-0.90704499
Mean26.365799
Median Absolute Deviation (MAD)4.2
Skewness-0.49089026
Sum57741.1
Variance31.971452
MonotonicityNot monotonic
2025-03-17T00:59:08.514263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 43
 
2.0%
21.3 42
 
1.9%
31.2 40
 
1.8%
32 37
 
1.7%
33 37
 
1.7%
32.8 36
 
1.6%
28.2 33
 
1.5%
33.3 32
 
1.5%
29.1 32
 
1.5%
30.6 30
 
1.4%
Other values (209) 1828
83.5%
ValueCountFrequency (%)
10.4 2
0.1%
10.8 1
< 0.1%
10.9 2
0.1%
11.2 1
< 0.1%
11.3 1
< 0.1%
11.6 1
< 0.1%
12.1 1
< 0.1%
12.2 1
< 0.1%
12.5 2
0.1%
12.9 1
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
35.8 2
 
0.1%
35.6 2
 
0.1%
35.4 2
 
0.1%
35.3 3
 
0.1%
35.2 2
 
0.1%
35.1 10
0.5%
35 3
 
0.1%
34.8 4
 
0.2%
34.7 5
0.2%

temparature
Real number (ℝ)

High correlation 

Distinct198
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.953059
Minimum7.4
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:08.823234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile15
Q119.3
median25.5
Q328.4
95-th percentile30.3
Maximum31.5
Range24.1
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation5.2224099
Coefficient of variation (CV)0.21802684
Kurtosis-0.83511905
Mean23.953059
Median Absolute Deviation (MAD)3.9
Skewness-0.55747106
Sum52457.2
Variance27.273565
MonotonicityNot monotonic
2025-03-17T00:59:09.249993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.1 55
 
2.5%
28.1 49
 
2.2%
30 48
 
2.2%
25.8 44
 
2.0%
25.5 38
 
1.7%
26.8 36
 
1.6%
29 34
 
1.6%
24.8 32
 
1.5%
23.1 31
 
1.4%
27.9 31
 
1.4%
Other values (188) 1792
81.8%
ValueCountFrequency (%)
7.4 1
 
< 0.1%
8.3 1
 
< 0.1%
8.5 1
 
< 0.1%
8.6 1
 
< 0.1%
8.7 1
 
< 0.1%
8.9 1
 
< 0.1%
10.1 1
 
< 0.1%
10.2 1
 
< 0.1%
10.4 3
0.1%
11.3 2
0.1%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
31.4 3
 
0.1%
31.3 3
 
0.1%
31.1 3
 
0.1%
31 11
 
0.5%
30.9 2
 
0.1%
30.8 9
 
0.4%
30.7 16
0.7%
30.6 28
1.3%
30.5 4
 
0.2%

mintemp
Real number (ℝ)

High correlation 

Distinct199
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.170091
Minimum4
Maximum29.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:09.584497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13.245
Q117.7
median23.85
Q326.4
95-th percentile28.2
Maximum29.8
Range25.8
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation5.0591201
Coefficient of variation (CV)0.22819573
Kurtosis-0.60825057
Mean22.170091
Median Absolute Deviation (MAD)3.45
Skewness-0.6491786
Sum48552.5
Variance25.594697
MonotonicityNot monotonic
2025-03-17T00:59:09.909248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.8 52
 
2.4%
24.8 51
 
2.3%
26.4 48
 
2.2%
28.1 42
 
1.9%
26.9 42
 
1.9%
28 42
 
1.9%
24.5 40
 
1.8%
17.2 38
 
1.7%
25.4 35
 
1.6%
26.5 34
 
1.6%
Other values (189) 1766
80.6%
ValueCountFrequency (%)
4 1
 
< 0.1%
4.7 1
 
< 0.1%
7 4
0.2%
7.5 1
 
< 0.1%
7.7 2
0.1%
8.1 2
0.1%
8.8 1
 
< 0.1%
9.4 1
 
< 0.1%
9.6 2
0.1%
9.8 2
0.1%
ValueCountFrequency (%)
29.8 1
 
< 0.1%
29.3 2
 
0.1%
29.2 2
 
0.1%
29.1 2
 
0.1%
29 8
0.4%
28.9 8
0.4%
28.8 7
0.3%
28.7 15
0.7%
28.6 11
0.5%
28.5 4
 
0.2%

dewpoint
Real number (ℝ)

High correlation 

Distinct218
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.454566
Minimum-0.3
Maximum26.7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size17.2 KiB
2025-03-17T00:59:10.323137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.3
5-th percentile10.79
Q116.8
median22.15
Q325
95-th percentile26
Maximum26.7
Range27
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation5.2884062
Coefficient of variation (CV)0.25854404
Kurtosis0.41827814
Mean20.454566
Median Absolute Deviation (MAD)3.25
Skewness-0.99788917
Sum44795.5
Variance27.96724
MonotonicityNot monotonic
2025-03-17T00:59:10.663269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.4 70
 
3.2%
25.3 66
 
3.0%
25 56
 
2.6%
25.8 54
 
2.5%
24.3 46
 
2.1%
25.9 46
 
2.1%
23.2 44
 
2.0%
23.1 41
 
1.9%
24.8 41
 
1.9%
19.9 39
 
1.8%
Other values (208) 1687
77.0%
ValueCountFrequency (%)
-0.3 1
< 0.1%
0.2 1
< 0.1%
1 1
< 0.1%
1.1 1
< 0.1%
1.7 1
< 0.1%
2 2
0.1%
2.2 1
< 0.1%
2.3 1
< 0.1%
2.4 1
< 0.1%
2.5 1
< 0.1%
ValueCountFrequency (%)
26.7 9
 
0.4%
26.6 5
 
0.2%
26.5 4
 
0.2%
26.4 22
1.0%
26.3 25
1.1%
26.2 15
 
0.7%
26.1 25
1.1%
26 32
1.5%
25.9 46
2.1%
25.8 54
2.5%

humidity
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.03653
Minimum39
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:10.981418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile70
Q177
median82
Q388
95-th percentile94
Maximum98
Range59
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.8006535
Coefficient of variation (CV)0.095087561
Kurtosis1.3991383
Mean82.03653
Median Absolute Deviation (MAD)5
Skewness-0.56154109
Sum179660
Variance60.850195
MonotonicityNot monotonic
2025-03-17T00:59:11.287348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
79 192
 
8.8%
81 139
 
6.3%
75 133
 
6.1%
78 121
 
5.5%
84 119
 
5.4%
89 114
 
5.2%
82 108
 
4.9%
87 106
 
4.8%
86 102
 
4.7%
76 89
 
4.1%
Other values (39) 967
44.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
45 1
 
< 0.1%
46 2
 
0.1%
47 1
 
< 0.1%
49 1
 
< 0.1%
52 4
0.2%
54 1
 
< 0.1%
56 1
 
< 0.1%
58 5
0.2%
59 9
0.4%
ValueCountFrequency (%)
98 14
 
0.6%
97 20
 
0.9%
96 20
 
0.9%
95 55
2.5%
94 22
 
1.0%
93 59
2.7%
92 66
3.0%
91 71
3.2%
90 81
3.7%
89 114
5.2%

cloud
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.721918
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:11.692074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38
Q169
median83
Q388
95-th percentile95
Maximum100
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.026498
Coefficient of variation (CV)0.23806182
Kurtosis1.0538992
Mean75.721918
Median Absolute Deviation (MAD)5
Skewness-1.3392745
Sum165831
Variance324.95461
MonotonicityNot monotonic
2025-03-17T00:59:12.090428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 375
 
17.1%
84 129
 
5.9%
83 127
 
5.8%
78 95
 
4.3%
81 88
 
4.0%
85 76
 
3.5%
82 65
 
3.0%
86 59
 
2.7%
95 57
 
2.6%
89 57
 
2.6%
Other values (68) 1062
48.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
7 1
 
< 0.1%
11 2
 
0.1%
17 3
 
0.1%
19 12
0.5%
20 9
0.4%
21 3
 
0.1%
22 4
 
0.2%
23 3
 
0.1%
25 3
 
0.1%
ValueCountFrequency (%)
100 37
1.7%
99 7
 
0.3%
97 6
 
0.3%
96 17
 
0.8%
95 57
2.6%
94 15
 
0.7%
93 33
1.5%
92 12
 
0.5%
91 42
1.9%
90 30
1.4%

sunshine
Real number (ℝ)

High correlation  Zeros 

Distinct120
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7444292
Minimum0
Maximum12.1
Zeros337
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:12.481591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4
median2.4
Q36.8
95-th percentile10.2
Maximum12.1
Range12.1
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation3.6263267
Coefficient of variation (CV)0.96845914
Kurtosis-1.0161999
Mean3.7444292
Median Absolute Deviation (MAD)2.4
Skewness0.63987141
Sum8200.3
Variance13.150245
MonotonicityNot monotonic
2025-03-17T00:59:12.877594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 337
 
15.4%
0.1 69
 
3.2%
0.3 63
 
2.9%
0.2 58
 
2.6%
0.6 55
 
2.5%
1 52
 
2.4%
1.2 51
 
2.3%
2.2 43
 
2.0%
0.4 42
 
1.9%
1.6 42
 
1.9%
Other values (110) 1378
62.9%
ValueCountFrequency (%)
0 337
15.4%
0.1 69
 
3.2%
0.2 58
 
2.6%
0.3 63
 
2.9%
0.4 42
 
1.9%
0.5 30
 
1.4%
0.6 55
 
2.5%
0.7 19
 
0.9%
0.8 5
 
0.2%
0.9 14
 
0.6%
ValueCountFrequency (%)
12.1 1
 
< 0.1%
12 1
 
< 0.1%
11.9 2
 
0.1%
11.8 8
0.4%
11.7 1
 
< 0.1%
11.5 3
 
0.1%
11.4 5
0.2%
11.2 7
0.3%
11.1 10
0.5%
11 9
0.4%

winddirection
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.86315
Minimum10
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:13.104298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q140
median70
Q3200
95-th percentile230
Maximum300
Range290
Interquartile range (IQR)160

Descriptive statistics

Standard deviation80.002416
Coefficient of variation (CV)0.76292211
Kurtosis-1.0718751
Mean104.86315
Median Absolute Deviation (MAD)35
Skewness0.70806298
Sum229650.3
Variance6400.3865
MonotonicityNot monotonic
2025-03-17T00:59:13.296935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
70 273
12.5%
220 241
11.0%
20 238
10.9%
40 230
10.5%
50 199
9.1%
80 178
8.1%
60 152
 
6.9%
230 142
 
6.5%
30 70
 
3.2%
200 58
 
2.6%
Other values (25) 409
18.7%
ValueCountFrequency (%)
10 47
 
2.1%
15 1
 
< 0.1%
20 238
10.9%
25 3
 
0.1%
30 70
 
3.2%
40 230
10.5%
50 199
9.1%
60 152
6.9%
65 1
 
< 0.1%
70 273
12.5%
ValueCountFrequency (%)
300 7
 
0.3%
290 10
 
0.5%
280 9
 
0.4%
270 10
 
0.5%
260 2
 
0.1%
250.3 1
 
< 0.1%
250 9
 
0.4%
240 43
 
2.0%
230 142
6.5%
220 241
11.0%

windspeed
Real number (ℝ)

Distinct223
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.804703
Minimum4.4
Maximum59.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2025-03-17T00:59:13.494987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile9.1
Q114.125
median20.5
Q327.9
95-th percentile39.7
Maximum59.5
Range55.1
Interquartile range (IQR)13.775

Descriptive statistics

Standard deviation9.8986588
Coefficient of variation (CV)0.45396897
Kurtosis0.28531768
Mean21.804703
Median Absolute Deviation (MAD)6.7
Skewness0.7693896
Sum47752.3
Variance97.983445
MonotonicityNot monotonic
2025-03-17T00:59:13.717566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5 35
 
1.6%
21.9 33
 
1.5%
25.1 32
 
1.5%
20.5 32
 
1.5%
25 31
 
1.4%
17.2 29
 
1.3%
39.5 28
 
1.3%
9.1 27
 
1.2%
24.4 27
 
1.2%
15.3 27
 
1.2%
Other values (213) 1889
86.3%
ValueCountFrequency (%)
4.4 1
 
< 0.1%
4.5 6
0.3%
5.5 2
 
0.1%
5.7 2
 
0.1%
5.9 2
 
0.1%
6.1 3
0.1%
6.6 4
0.2%
6.9 3
0.1%
7.3 3
0.1%
7.4 6
0.3%
ValueCountFrequency (%)
59.5 4
0.2%
55.5 6
0.3%
52.8 5
0.2%
50.7 4
0.2%
50.6 9
0.4%
48 6
0.3%
46.3 4
0.2%
44.7 8
0.4%
43.8 9
0.4%
43.1 6
0.3%

rainfall
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
1
1650 
0
540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2190
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Length

2025-03-17T00:59:13.898998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T00:59:14.112488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Most occurring characters

ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1650
75.3%
0 540
 
24.7%

Interactions

2025-03-17T00:59:03.833301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:37.922950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:40.318452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:44.120087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:46.215710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:48.410964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:50.824085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:52.999937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:56.115809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:59.517035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:01.633296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:03.999822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:34.908856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:40.556250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:44.279850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:48.582176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:59.685565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:01.830873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:04.167098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:35.115515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:38.254023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:40.873423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:44.448439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:44.809201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:35.816192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:38.948210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:42.128135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:45.126805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:47.257709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:51.914839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:54.340510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:57.885767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:59:02.757124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:05.004987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:54.611106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:58.187523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:59:02.925790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:05.173174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:36.158415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:39.296838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:42.767099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:50.133680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T00:58:58.460453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:00.912727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:03.101125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:05.341581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:37.451455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:39.480943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:43.072102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:45.661948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:47.825730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:50.297497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:52.444992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:55.184286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:58.660363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:01.092353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:03.261289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:05.505972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:37.600443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:39.714637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:43.677819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:45.862090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:48.027483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:50.496768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:52.642579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:55.501127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:58.824161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:01.257403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:03.433779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:05.674698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:37.746847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:40.006239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:43.935603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:46.037470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:48.206792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:50.673413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:52.822547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:55.835634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:58:59.347778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:01.433863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T00:59:03.625927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-17T00:59:14.329713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
clouddaydewpointhumidityidmaxtempmintemppressurerainfallsunshinetemparaturewinddirectionwindspeed
cloud1.000-0.115-0.2470.603-0.005-0.434-0.3810.2040.641-0.828-0.409-0.1840.252
day-0.1151.0000.104-0.1020.1530.1310.1280.0170.1780.0940.1270.0590.009
dewpoint-0.2470.1041.0000.0420.0090.9020.925-0.8320.1470.3710.9220.717-0.306
humidity0.603-0.1020.0421.000-0.028-0.153-0.092-0.0550.494-0.558-0.1220.0220.083
id-0.0050.1530.009-0.0281.0000.0110.018-0.0080.073-0.0030.0130.0020.014
maxtemp-0.4340.1310.902-0.1530.0111.0000.961-0.8130.1670.5530.9820.677-0.351
mintemp-0.3810.1280.925-0.0920.0180.9611.000-0.8270.1610.4900.9810.701-0.325
pressure0.2040.017-0.832-0.055-0.008-0.813-0.8271.0000.152-0.326-0.830-0.6380.275
rainfall0.6410.1780.1470.4940.0730.1670.1610.1521.0000.5760.1690.1030.144
sunshine-0.8280.0940.371-0.558-0.0030.5530.490-0.3260.5761.0000.5230.286-0.289
temparature-0.4090.1270.922-0.1220.0130.9820.981-0.8300.1690.5231.0000.693-0.341
winddirection-0.1840.0590.7170.0220.0020.6770.701-0.6380.1030.2860.6931.000-0.158
windspeed0.2520.009-0.3060.0830.014-0.351-0.3250.2750.144-0.289-0.341-0.1581.000

Missing values

2025-03-17T00:59:05.932383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-17T00:59:06.145721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddaypressuremaxtemptemparaturemintempdewpointhumiditycloudsunshinewinddirectionwindspeedrainfall
0011017.421.220.619.919.487.088.01.160.017.21
1121019.516.216.915.815.495.091.00.050.021.91
2231024.119.416.114.69.375.047.08.370.018.11
3341013.418.117.816.916.895.095.00.060.035.61
4451021.821.318.415.29.652.045.03.640.024.80
5561022.720.618.616.512.579.081.00.020.015.71
6671022.819.518.415.311.356.046.07.620.028.40
7781019.715.813.612.711.896.0100.00.050.052.81
8891017.417.616.515.612.586.0100.00.050.037.51
99101025.416.514.412.08.677.084.01.050.038.30
iddaypressuremaxtemptemparaturemintempdewpointhumiditycloudsunshinewinddirectionwindspeedrainfall
218021803561017.122.320.819.118.976.078.01.540.014.21
218121813571021.822.920.819.215.576.049.09.350.035.60
218221823581022.618.516.815.812.180.088.01.330.024.41
218321833591016.918.416.415.313.679.084.02.440.038.01
218421843601015.820.918.817.617.778.088.00.020.030.61
218521853611014.623.220.619.119.997.088.00.140.022.11
218621863621012.417.217.316.315.391.088.00.050.035.31
218721873631013.319.016.314.312.679.079.05.040.032.91
218821883641022.316.415.213.814.792.093.00.140.018.01
218921893651013.821.219.118.018.089.088.01.070.048.01